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PurposeShort-term changes in consumers' shopping behaviour due to the Covid-19 pandemic have been studied, but not the long-term effects. This study fills this gap by exploring the long-term changes in consumers' retail shopping behaviour, due to their experiences of the Covid-19 pandemic.Design/methodology/approachQualitative data were collected from one hundred fifty-nine respondents, and grounded theory approach was applied for interpretation. Gioia thematic analysis method, open coding, and axial coding were used for analysis.FindingsIndividuals who positively approached their experiences during the Covid-19 demonstrated increased pro-sustainable and pro-environmental self-identity, resulting in sustainable consumption and a shift to online shopping. Individuals having overpowering negative experiences demonstrated heightened fear of missing out (FOMO), loss aversion, and rumination. While shopping, they demonstrated herd behaviour and shifted to online shopping.Research limitations/implicationsThis study highlights emotional and psychological mechanisms influencing long-term changes in consumer shopping preferences post Covid-19 pandemic. The generalizability of the findings is limited due to the study's exploratory nature and the sample size.Originality/valueThis study contributes to shopping behaviour literature by uncovering novel constructs of self-identity, loss aversion, FOMO, and rumination as antecedents to long-term shopping behaviour changes post-Covid-19. It provides a new conceptual model of consumers' shopping behaviour, which may be empirically validated.

PurposeGenerative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.Design/methodology/approachWe conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.FindingsResults show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.Originality/valueConsumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

A bstract We study the phases of Yukawa theories at weak coupling and the Gross-Neveu models in AdS spaces at zero and finite temperature. Following the method used in [15], we first compute the one-loop partition functions, using the generalized eigenfunctions of the Dirac and Laplace operators on Euclidean AdS in the Poincaré coordinates. These functions satisfy desired periodicities under thermal identification. The method replicates results for partition functions known in the literature. We then study the phases of these field theories with fermions as regions in the corresponding parameter spaces at zero temperature. The phases and the corresponding phase boundaries are further identified as a function of the mass-squared of the scalar field and temperature for the Yukawa theories. While for the Gross-Neveu models, the changes in the phases as a function of the fermionic mass and the coupling constant at finite temperature are discussed. The Gross-Neveu-Yukawa model is studied for AdS 4 . We also note certain deviations from phases of these theories in flat space.

Plasmodium sporozoites are the crescent-shaped forms of malaria parasites injected from the salivary glands of mosquitoes into the skins of their vertebrate hosts. To proceed towards the liver of the host, sporozoites individually migrate at very high speeds and with relatively few adhesive interactions. By contrast, in the mosquito sporozoites often exist as collectives. Here we study their motion in collectives extracted from salivary glands, a situation in which dozens of sporozoites form rotating vortices. Complementing our experiments with quantitative image analysis and agent-based computer simulations, we find that, owing to their mechanical flexibility, single sporozoites are sorted according to their curvatures and speeds, and that these effects increase with vortex size. We also find that the vortices undergo oscillatory breathing because the thrust from the motility force of the single sporozoites can be stored in their elastic energy. Our findings suggest that the malaria parasite has evolved flexibility as an essential means to adapt to its mechanical environment and to ensure efficient transmission. In general, our work demonstrates how single-particle shape and mechanics can determine the dynamics of large, active collectives. The collective motion of malaria parasites is analyzed as a model system for active elastic matter and suggests that mechanical flexibility is favourable for parasite transmission.

Synthesis and characterization of dual functioning material is an effective approach for the promotion of organic pollutant degradation through adsorption as well as photocatalysis. Herein, graphene oxide was modified by the addition of barium nitrate and iron to construct a smooth sheet-like structure (BaFe 2 O 4 /rGO) for the removal of chlorhexidine digluconate (CHD). Compared with GO (75.69%—UV light; 88.17%—visible light), BaFe 2 O 4 /rGO showed significant adsorption-photocatalysis effect under visible light (93.95%) than that under UV light (78.17%). The introduction of barium nitrate and iron into graphene oxide leads to a smooth porous structure with increased surface area (93.66 m 2  g −1 ), which resulted in a large number of adsorption active sites and great photocatalytic activity with efficient charge separation. Although catalysts did not mineralize CHD completely, but the parent compound mineralized to some extent, which was confirmed by the TOC measurement and UV 254 absorbance variation. In addition, toxicity of degraded products was analysed by bacterial susceptibility test on Bacillus cereus DPAML065, suggesting that nontoxic by-products of CHD were formed, which leads to their safe disposal. Based on the identified transformed products, the possible degradation pathway was proposed. Batch studies demonstrated that BaFe 2 O 4 /rGO is highly photoactive based on reaction rate constant ( R 2  = 0.984), where the kinetics data were well-fitted using the pseudo-first order. Moreover, efficiency of catalysts was examined under solar light to achieve the sustainability. Graphical abstract

Recognizing speech emotions is indeed a crucial aspect of human–computer interaction. However, developing a model that can accurately process multiple languages is one of the challenging tasks. The feature selection process plays a vital role in multilingual speech emotion recognition because it helps to reduce irrelevant features from each language, ultimately enhancing the performance of the model. This research aims to address this task in a more precise way. It achieves this by employing Grid Search based Principal Component Analysis and an ensemble voting classifier for multilingual speech emotion recognition. Here we mention three essential steps of recognizing emotion from a multilingual dataset. The first step involves feature extraction from speech signals, such as MFCC, root-mean-square, ZCR, flux, roll-off, Centroid, bandwidth, chroma, and fundamental frequency. The second step entails the selection of an essential feature subset by removing redundant and unnecessary features using Principal Component Analysis. We also utilize the Grid Search technique to determine the feature subset that would yield the highest accuracy. The third step encompasses SVM and Random Forest, that are widely recognized classifiers. Additionally, we propose an ensemble voting classifier. Our study compares the performance of these classifiers on three distinct corpora—RAVDESS, EMOVO, and SUBESCO with and without the feature selection strategy. The accuracy for RAVDESS EMOVO and SUBESCO dataset 74.30%, 79.66%, 87.64%, respectively. After comparing our proposed approach with other approaches mentioned in the literature survey, it became evident that our approach outperforms the rest.

A bstract We extend the computation of one-loop partition function in AdS d +1 using the method in [ 23 ] and [ 24 ] for scalars and fermions to the case of U(1) vectors. This method utilizes the eigenfunctions of the AdS Laplacian for vectors. For finite temperature, the partition function is obtained by generalizing the eigenfunctions so that they are invariant under the quotient group action, which defines the thermal AdS spaces. The results obtained match with those available in the literature. As an application of these results, we then analyze phases of scalar QED theories at one-loop in d = 2, 3. We do this first as functions of AdS radius at zero temperature showing that the results reduce to those in flat space in the large AdS radius limit. Thereafter the phases are studied as a function of the scalar mass and temperature. We also derive effective potentials and study phases of the scalar QED theories with N scalars.